Evaluation of the Classifiers in Multiparameter and Imbalanced Data Sets

被引:0
作者
Piotrowska, Ewelina [1 ]
机构
[1] Opole Univ Technol, Proszkowska 76 St, PL-45758 Opole, Poland
来源
INFORMATION SYSTEMS ARCHITECTURE AND TECHNOLOGY, ISAT 2019, PT II | 2020年 / 1051卷
关键词
Imbalanced data; Optimization methods; Classification;
D O I
10.1007/978-3-030-30604-5_24
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
The paper discusses the basic problems resulting from the classification of imbalanced data, which are additionally described by a large number of parameters. The paper also presents various optimization methods, including the use of a synthetic indicator that is the product of specificity and the power of sensitivity, which was proposed by the author.
引用
收藏
页码:263 / 273
页数:11
相关论文
共 50 条
  • [41] A memetic approach for training set selection in imbalanced data sets
    Bahareh Nikpour
    Hossein Nezamabadi-pour
    International Journal of Machine Learning and Cybernetics, 2019, 10 : 3043 - 3070
  • [42] On Machine Learning with Imbalanced Data and Research Quality Evaluation Methodologies
    Lipitakis, Anastasia-Dimitra
    Lipitakis, Evangelia A. E. C.
    2014 INTERNATIONAL CONFERENCE ON COMPUTATIONAL SCIENCE AND COMPUTATIONAL INTELLIGENCE (CSCI), VOL 1, 2014, : 451 - 457
  • [43] High dimensional classifiers in the imbalanced case
    Bak, Britta Anker
    Jensen, Jens Ledet
    COMPUTATIONAL STATISTICS & DATA ANALYSIS, 2016, 98 : 46 - 59
  • [44] To Combat Multiclass Imbalanced Problems by Aggregating Evolutionary Hierarchical Classifiers
    Ning, Zhihan
    Jiang, Zhixing
    Zhang, David
    IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2024, : 1 - 15
  • [45] Difficulty Factors and Preprocessing in Imbalanced Data Sets: An Experimental Study on Artificial Data
    Wojciechowski S.
    Wilk S.
    1600, Walter de Gruyter GmbH (42): : 149 - 176
  • [46] Affinity and class probability-based fuzzy support vector machine for imbalanced data sets
    Tao, Xinmin
    Li, Qing
    Ren, Chao
    Guo, Wenjie
    He, Qing
    Liu, Rui
    Zou, Junrong
    NEURAL NETWORKS, 2020, 122 (122) : 289 - 307
  • [47] DatRel: a noise-tolerant data relocation approach for effective synthetic data generation in imbalanced classifiers
    Saglam, Fatih
    MACHINE LEARNING, 2025, 114 (05)
  • [48] An efficiency curve for evaluating imbalanced classifiers considering intrinsic data characteristics: Experimental analysis
    Chao, Xiangrui
    Kou, Gang
    Peng, Yi
    Fernandez, Alberto
    INFORMATION SCIENCES, 2022, 608 : 1131 - 1156
  • [49] GAAE: a novel genetic algorithm based on autoencoder with ensemble classifiers for imbalanced healthcare data
    Pintu Kumar Ram
    Pratyay Kuila
    The Journal of Supercomputing, 2023, 79 : 541 - 572
  • [50] An Effective Over-sampling Method for Imbalanced Data Sets Classification
    Zhai Yun
    Ma Nan
    Ruan Da
    An Bing
    CHINESE JOURNAL OF ELECTRONICS, 2011, 20 (03): : 489 - 494